Abstract
In this paper, we present a new solution for extracting key frames from motion capture data using an optimization algorithm to obtain compact and sparse key frame data that can represent the original dense human body motion capture animation. The use of the genetic algorithm helps determine the optimal solution with global exploration capability while the use of a probabilistic simplex method helps expedite the speed of convergence. By finding the chromosome that maximizes the fitness function, the algorithm provides the optimal number of key frames as well as the low reconstruction error with an ordinary interpolation technique. The reconstruction error is computed between the original motion and the reconstruction one by the weighted differences of joint positions and velocities. The resulting set of key frames is obtained by iterative application of the algorithm with initial populations generated randomly and intelligently. We also present experiments which demonstrate that the method can effectively extract key frames with a high compression ratio and reconstruct all other non key frames with high quality.
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Acknowledgements
This work is supported and funded by the State Key Program of National Natural Science of China (No. 60533070), the Special Foundation of the “211 Project” Subject Construction for Young Researcher at the Beijing University of Technology, the Beijing Municipal Commission of Education of Science and Technology Program (No. KM200910005020). We would like to acknowledge the help of the CMU Graphics Lab, who provided us with the human motion capture data. We also would like to thank the anonymous reviewers for their helpful comments and suggestions.
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Liu, Xm., Hao, Am. & Zhao, D. Optimization-based key frame extraction for motion capture animation. Vis Comput 29, 85–95 (2013). https://doi.org/10.1007/s00371-012-0676-1
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DOI: https://doi.org/10.1007/s00371-012-0676-1